Value Maximizing Recommendation Systems

ABSTRACT

A server determines a plurality of immediate candidate items for a first web page to recommend to a user. For each particular immediate candidate item of the plurality of immediate candidate items, the server determines a separate sequence of two or more subsequent possible candidate items for subsequent web pages to recommend to the user in the event that the user selects the particular immediate candidate item. Further, the server selects a particular immediate candidate item from the plurality of immediate candidate items for the first web page to recommend to the user. The first web page that recommends the plurality of immediate candidate items is generated and sent over the Internet to the user.

FIELD OF THE INVENTION

The present invention relates to candidate item recommendation systemsand, more specifically, to a technique for determining which items torecommend to customers at the present time, by evaluating trees ofsubsequent items that could be recommended to the customers in thefuture, wherein the trees of subsequent items are generated for eachimmediate item available for recommendation to the customers at thepresent time.

BACKGROUND

A growing popularity of the Internet has spurred a rapid growth ofInternet websites that promote various products and services to theusers. For example, Internet-based shopping websites advertise,recommend, and sell books, clothing items, jewelry items, home goodsitems, etc. Movie-rental websites distribute copies of movies. Newswebsites market copies of electronic documents, access to newsgroups,access to news media, etc. Game websites promote and sell electronicgames, memberships to fan clubs, etc. Music websites sell music files,music media, etc. Travel websites sell airline tickets, vacationpackages, etc. Entertainment websites sell tickets to concerts, shows,etc. Websites affiliated with social organizations recommend candidatesfor a board election, etc. These and many other types of websitesattempt to promote their candidate items, objects, services, products,people, and/or other entities to the users.

Regardless of the nature of the candidate items promoted by a website,the candidate items are selected by determining those candidate itemsthat meet specific objectives specified by the website designers orsponsors. For example, one objective may be to maximize the number ofsales transacted by the website. In a book-selling example, theobjective may be accomplished by recommending, to the users, the booksthat are bestsellers and that are most likely going to be purchased bythe customers. In a travel-package-selling example, the objective may beaccomplished by recommending, to the users, the travel packages that arethe most popular and are in a highest demand.

Another objective may be to maximize the revenue generated from thesales of products by the website. In the book-selling example, theobjective may be accomplished by recommending the books that are notonly very popular, but also most expensive. Similarly, in thetravel-package-selling example, the objective may be accomplished byrecommending the travel packages that are not only in high demand, butalso luxurious.

However, in formulating the objectives, the existing recommendingsystems rely on approaches that merely take into a consideration thecandidate items that are presently recommended to the users. In thebook-selling example, the system selects and simultaneously recommends,to the user, one or more book candidates, hoping that the objective ismet when the user selects and/or purchases one of the simultaneously andpresently recommended books.

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements and in which:

FIG. 1 depicts an example of a system in which an embodiment of theinvention may be implemented;

FIG. 2 depicts an example of technique for determining candidate itemrecommendations to a user, by determining immediate candidate items thatare suitable for recommendation presently, and determining one or moresubsequent candidate items that would be suitable for recommendation inthe future in the event that the immediate candidate item of thatsequence actually is presently recommended, in accordance with anembodiment of the invention; and

FIG. 3 is a block diagram of a computer system on which embodiments ofthe invention may be implemented.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

Overview

It has been observed that the users who follow recommendations presentedby a website do so not just sporadically or irregularly, but habituallyand quite frequently. For example, a user who purchased a recommendedbook from one website often continues purchasing the recommended booksfrom the same website. Furthermore, if one customer purchased a copy ofa first book and then subsequently purchased a copy of a second book,then, quite often, another customer who purchased a copy of the firstbook eventually will also purchase a copy of the second book. Moreover,if two customers purchased two or more books in a particular order,then, most likely, other customers will purchase the same two or morebooks in that same particular order.

Techniques described herein look beyond the immediate selection ofcandidate items. In determining which candidate item recommendationsought to be made at a particular moment, a recommendation process takesinto consideration not only the candidate items that are suitable forrecommendation in the present, but also possible subsequentrecommendations that will become beneficial for making in the future asa consequence of certain candidate items being recommended in thepresent. The selection of candidate items for recommendation describedherein is based on not only the individual characteristics of thecandidate items themselves, but also on the potential sequences of eventselection that are predicted to follow the event of presentlyrecommending the candidate items to the user.

Techniques described herein determine which items to recommend tocustomers by generating and evaluating trees of subsequent items thatcould be recommended to the customers in the future. The trees ofsubsequent items are generated for each immediate item available forrecommendation to the customers at the present time.

The process of generating the trees of subsequent items is recursive. Itstarts from selecting one or more immediate candidate items availablefor recommendation to the customer at the present moment. Then, for eachimmediate candidate item, the process determines first-tier-subsequentitems that could be recommended to the customer immediately after thecustomer hypothetically selected the particular immediate item. Then,for each immediate candidate item and for each subsequent item, theprocess determines second-tier-subsequent items that could berecommended to the customer immediately after the customerhypothetically selected the particular first-tier-subsequent item. Theprocess is repeated until all trees for all immediate candidate itemsare generated and evaluated, and the best immediate item from theimmediate candidate items is selected.

The described recommendation system may be perceived as a navigationsystem that takes into consideration several possible sequences ofevents, and evaluates each of the possible sequences by computing aresult of a specified value function for each of the possible sequencesas if the events in the possible sequences indeed have taken place.

The recommendation system determines, for each particular candidate itemthat is suitable for recommendation to a customer through a web page atthe present time, a separate sequence (or tree of sequences) ofsubsequent possible candidate items that might be beneficiallyrecommended to the customer through web pages to be subsequentlypresented to the customer at future times in the event that the customerselected the particular immediate candidate item.

The recommendation system uses statistical information about thecustomers (also referred to as “users”), and the candidate items todetermine the subsequent candidate items that the customers are likelyto select after selecting any of the particular candidate items, andthen after selecting any of the subsequent candidate items, and soforth. Thus, at any given time, a tree of potential future beneficialrecommendations may be generated dynamically based at least in part onthe previously recommended candidate items actually selected by thecustomer.

Recommendation Selection

In one embodiment, the candidate items to be recommended presently areselected from a pool of candidate items that are suitable forrecommendation to a user at the present time. These candidate items arehereinafter referred to as “immediate candidate items.”

The aspect of “selecting” a particular candidate item may be interpretedrather narrowly as merely a selection of the particular immediatecandidate item by the user, or more broadly as a purchase of therecommended candidate item by the user.

The described approach takes into consideration not only the immediatecandidate items that a web page may beneficially recommend to a user ina first step, but also the subsequent candidate items that the web pagemay beneficially recommend to the user in the steps following thepresentation of the selected immediate candidate items.

After evaluating the immediate candidate items and the subsequentcandidate items, a subset of the immediate candidate items is selectedand recommended to the user in the first step. The selected immediatecandidate items are herein after referred to as “particular candidateitems.”

In one embodiment, the selection of the immediate candidate items,subsequent candidate items and particular candidate items is based on(1) the information about the user to whom the recommendation is beingmade, (2) the individual characteristics of the immediate candidateitems, and (3) the information about the sequences of subsequentcandidate items that the website may recommend to the user in subsequentsteps in the future.

In one embodiment, a server determines a set of immediate candidateitems to recommend to a user through a first web page that is to bepresented to the user by taking into account a user profile, statisticalinformation, a history log (e.g., a log of web pages and/or candidateitems that the user previously visited and/or selected), purchasinghabits, purchasing preferences, purchasing history, interests, aprofession, etc.

In determining the set of immediate candidate items, the server may alsotake into account candidate items' statistical information, such asstatistical information about the candidate items' popularity, costs,prices, values, availability, and any additional information that mightbe available about the candidate items.

The server may also take into account information about the likelihoodthat, if recommended, a candidate item will be indeed selected and/orpurchased by the user. For example, the server may utilize informationreflecting the quantity of the users who, after selecting or purchasinga particular first book, also purchased a particular second book. Suchinformation might, for example, indicate the ratio of (a) the quantityof users who purchased the particular second book after purchasing thefirst book to (b) the total quantity of users who purchased the firstbook.

In one embodiment, evaluation of separate sequences of subsequentpossible candidate items includes determining which separate sequencemay maximize the outcome expected from recommending a particularimmediate candidate item to the user. The outcome is maximized based onthe objective specified by the website designers or sponsors. Forexample, the outcome may be optimized to maximize the revenue generatedby the website, the quantity of the sold products, the quantity ofselections that the user makes before leaving the website, etc.

The server may maximize the outcome expected from recommending aparticular immediate candidate item by computing a value functionassociated with selecting the particular immediate candidate item andthe associated sequence of the subsequent possible candidate items. Forexample, the server may compute a value function that represents revenuegenerated by a sale of the particular immediate candidate item and thesubsequent possible candidate items. According to another example, theserver may compute a value function that represents a length of thesequence of the subsequent possible candidate items associated with theparticular immediate candidate item.

In one embodiment, the server generates and sends over the Internet afirst website that recommends the particular immediate candidate item tothe user. Alternatively, the server may generate and send over theInternet a first website that recommends two or more particularimmediate candidate items, each of which was selected according to theapproach described above.

Although embodiments of the invention are described in the context ofthe website-based candidate item recommendation systems, embodiments ofthe invention may also be applied to other types of the recommendationsystems. For example, the described approach may be applicable tooffline systems; television shopping networks; marketing tools, etc.

Example System

FIG. 1 depicts an example of a system in which an embodiment of theinvention may be implemented. The system of FIG. 1 includes arecommendation engine 110, a user database 102, and a candidate itemdatabase 104.

In one embodiment, recommendation engine 110 is communicatively coupledwith a server providing services to an Internet website. Recommendationengine 110 receives requests from the website server, processes therequest, generates a response to the request and provides the responseto the website server. Recommendation engine 110 may provide services toa plurality of website servers, each of which can send a number ofrequests to recommendation engine 110 and receive a response to each ofthe requests.

Recommendation engine 110 is communicatively coupled with one or moredatabase storages. The storage may be configured as a multi-devicesystem of storages. Alternatively, the database storages may beconfigured as one, centrally maintained storage system.

In one embodiment, recommendation engine 110 comprises a candidate itemselector 120, a sequence selector 130, a sequence evaluator 140 and awebpage generator 150. Candidate item selector 120, sequence selector130, sequence evaluator 140 and webpage generator 150 may be implementedas separate processes communicating with each other via communicationlinks implemented within recommendation engine 110. Alternatively, theselectors, evaluator and generator may be implemented as amulti-thread-process of recommendation engine 110. Candidate itemselector 120, sequence selector 130, sequence evaluator 140 and webpagegenerator 150 may be embodied in or executed on the same or on differentmachines, such as the computer depicted in FIG. 3 and described below.

Request for Recommendation

In one embodiment, a web site server sends a request to recommendationengine 110 requesting that recommendation engine 110 determine one ormore candidate items that a web page can recommend to a user. In abook-selling example, the website server may request that recommendationengine 110 determine one or more book titles that the website web pagecan recommend to a user.

While processing the request, recommendation engine 110 may collect,retrieve and use information about the user to whom the recommendationis to be made, information about the users who have interacted with thewebsite, etc. Furthermore, recommendation engine 110 may collect,retrieve and use information about the items that the websiteadvertises, recommends and/or sells to the users.

In on embodiment, the request received by recommendation engine 110 isspecific and provides information about the user to whom therecommendation is to be made. For example, the request may containinformation about the user profile, demographics, personalcharacteristics, interests, browsing history, purchasing preferences andhistory, etc. The request may also contain identifiers and/or links to adatabase and/or files that contain the user specific information.

The request may also contain information about the type of the candidateitems that could be presented to the user. For example, the request mayindicate that the term candidate items include merchandises, such asbooks, clothing items, household items, vacation packages, airlinetickets, memberships, etc., or include services, such as onlinereservations, access to electronic media, etc.

Further, the request may provide the website preferences with regard tothe website policy, guidelines, strategy and other information thatrecommendation engine 110 may find useful in determining the candidateitems for recommendation.

Examples of website servers may include the servers that recommend andtransact books to online users, the servers that recommend and sellvacation packages to the users, online shopping network servers, onlinenews network servers, etc.

Database Storage

In one embodiment, database storage comprises a user database 102, and acandidate item database 104. Databases 102-104 may be maintained andupdated synchronously or independently from each other. Databases102-104 may be implemented in one physical storage device, or in adistributed, multi-device storage.

User Database

In one embodiment, user database 102 stores various types of informationabout the users that have been interacting with recommendation engine110 and/or other recommendation engines. In a book-selling example, userdatabase 102 may store information about the users who purchased onlinebooks from one or more book-selling websites, user profiles of the userswho purchased online books, users' purchasing preferences, the titles ofbooks that the users have purchased, the frequency in which the userspurchase the books, the users' demographic information, the users'interests, the lengths of the online sessions during which the usersinteracted with the book-selling websites, etc.

Moreover, user database 102 may store information about statisticalinformation related to purchasing transactions. For example, userdatabase 102 may store information about the quantity of users who buybooks in a certain genre, the quantity of users who buy books written bya particular writer, the quantity of users who buy books published by acertain publishing company, etc.

In addition, user database 102 may store information about a probabilitythat a particular user will buy books in the certain genre, aprobability that the particular user will buy books written by aparticular writer, a probability that the particular user will buy bookspublished by a certain publishing company, etc.

Candidate Item Database

In one embodiment, candidate item database 104 stores various types ofinformation about the candidate items that recommendation engine 110 mayrecommend to users. In a book-selling example, candidate item database104 may store information about the bestsellers in each genre, thebestseller in each user group age, the bestseller for certain occasions,etc.

Candidate item database 104 may also contain information about each ofthe candidate items. For example, candidate item database 104 maycontain a price of each book, a number of available books that have thesame price, a number of books that belong to the same pricing category(e.g., inexpensive, moderately expensive, expensive, very expensive),etc.

In one embodiment, candidate item database 104 contains statisticalinformation about likelihood (or a probability) that a particularcandidate item, if recommended to a user, will be indeed selected orpurchased by the user. In a book-selling example, candidate itemdatabase 104 may contain, for each book that can be offered to theusers, a numerical value reflecting the probability that a particularbook, if recommended to the user, will be selected and/or purchased bythe user.

In one embodiment, for each book available for recommendation, candidateitem database 104 stores two or more probability values, wherein thefirst probability value may reflect the likelihood that a particularbook, if recommended to the user, will be selected by the user so theuser can be presented with another book recommendation, while the secondprobability value may reflect the likelihood that the particular book,if recommended to the user, will be not only selected, but alsopurchased by the user.

Value Function

A value function may correspond to a method that is used to computecosts for the candidate items. In a book-selling example, the valuefunction may be computed using such parameters as a price of the book, aquantity of users who purchased the book, etc. In one example, the valuecomputed using the value function may correspond to the revenuegenerated from the sales of a particular book, from the sales of a groupof particular books, from the sales of all the books transacted by thewebsite during a certain period of time, etc.

In a vacation-package-selling example, the value function may becomputed using such parameters as a price of the vacation package, aquantity of users who purchased the vacation package, etc. In oneexample, the value computed using the value function may correspond tothe revenue generated from the sales of a particular vacation package,from the sales of all vacation packages for a particular vacationdestination, from the sales of all vacation packages transacted by thewebsite during a certain period of time, etc.

In a news-group-membership example, the value function may be computedusing such parameters as a quantity of different membership options, aprice of each of the membership option, a quantity of memberships ineach membership options that has been sold to the users, etc.

In one embodiment, a value computed using the value function correspondsto the revenue generated from the sales of each membership option, fromthe sales of all membership that the website offers, from the sales ofthe memberships transacted by the website during a certain period totime, etc.

Probabilistic Value Function

Statistical information stored in candidate item database 104 may beused to compute a value of the probabilistic value function associatedwith the candidate items. The probabilistic value function may be usedto compute a probable cost of the candidate items. In a book-sellingexample, the probabilistic value function may be computed using suchparameters as a probability that the particular book could be purchasedby an individual user, a probability that the particular book could bepurchased by a certain group of users, a probability that the particularbook could be selected but not purchased by an individual user, etc.

In a membership-selling example, the probabilistic value function may becomputed using such parameters as a probability that the particular typeof membership could be purchased by an individual user, a probabilitythat, if an individual user purchased one type of membership, then hewill also renew the type of membership, a probability that theparticular type of membership could be purchased by a certain group ofusers, etc.

In one embodiment, a value computed using the probabilistic valuefunction corresponds to the probable revenue that could be generatedfrom the sales of the particular items or services. In a book-sellingexample, the value computed using the probabilistic value function maycorrespond to the probable revenue that could be generated from thesales of a particular book, from the sales of a group of particularbooks, or from the sales of all the books that could be transacted bythe website during a certain period of time, etc.

Candidate Item Selector

In one embodiment, candidate item selector 120 is a machine-implementedmodule that selects candidate items. Candidate item selector 120comprises a collector 122 and a processor 124. Collector 122 collects,requests and updates information about the candidate items, whileprocessor 124 processes the information supplied by collector 122, andprovides the information to sequence selector 130.

In one embodiment, candidate item selector 120 is invoked byrecommendation engine 110 to select one or more immediate candidateitems that could be recommended to a user at the present time. Candidateitem selector 120 may be also invoked to select one or more subsequentitems that could be recommended to the user in the future, which isafter the user hypothetically selected the immediate candidate itemsrecommended to the user. Thus, candidate item selector 120 is invoked toselect the immediate candidate items and during the process ofgenerating the trees of subsequent items that could be recommended tothe user in the future.

Collector 122 collects information from user database 102, and candidateitem database 104. For example, collector 122 may collect (or request tobe collected and provided) information about the user profile,purchasing preferences, demographic information, geographic location,purchasing history, etc.

Further, collector 122 may collect (or request to be collected andprovided) information about the candidate items that could berecommended to the user. In a book-selling example, collector 122 mayidentify the website catalog, determine the price of each of the booklisted in the catalog, retrieve statistical information about the listedbooks, sales of the books, etc.

Collector 122 provides the collected information to processor 124 andresponds to requests from processor 124 for any additional information.

In one embodiment, processor 124 receives information about theparticular user and the particular possible candidate items, processesthe received information, and sends the processed information tosequence selector 130.

Processor 124 selects the candidates that are most suitable for the userto whom the recommendation is to be made. For example, if collector 122provided information that the particular user is a twenty-year-old malewho likes sports, and that the possible candidate items includemagazines, then processor 124 selects sport magazines from the set ofpossible candidate items.

In one embodiment, processor 124 takes into consideration statisticalinformation associated with the candidate items stored in candidatedatabase 104. In the above sport-magazines example, after selecting allsport magazines, processor 124 would determine which of the sportmagazines the twenty-year-old male who likes sports would find mostinteresting.

Further, the processing performed by processor 124 may involverestructuring or reorganizing the collected information, andreformatting the information to a format that is compatible with theformat used by other modules of recommendation engine 110.

Processor 124 may communicate with sequence selector 130, sequenceevaluator 140 and webpage generator 150 each time any of them requiresany information about the candidate items or the users.

Sequence Selector

In one embodiment, sequence selector 130 is a machine-implemented modulethat, for each immediate candidate item, generates a tree of subsequentitems that could be recommended to the customers in the future, which isafter a user hypothetically selected the immediate candidate item.Sequence selector 130 comprises a collector 132 and a processor 134.Collector 132 collects, requests and updates information that pertainsto the selection of possible sequences of candidate items by sequenceselector 130, while processor 134 processes the information supplied bycollector 132, and provides the selected sequences of candidate items tosequence evaluator 140.

In one embodiment, sequence selector 130 is invoked by recommendationengine 110 after candidate item selector 120 determined one or moreimmediate candidate items for a web site to recommend to a user. Foreach immediate candidate item, sequence selector 130 determinesfirst-tier-subsequent items that could be recommended to the userimmediately after the user hypothetically selected the particularimmediate item. Then, for each immediate candidate item and for eachsubsequent item, sequence selector 130 determines second-tier-subsequentitems that could be recommended to the user immediately after the userhypothetically selected the particular first-tier-subsequent item. Theprocess is repeated until all trees for all immediate candidate itemsare generated.

The collector 132 usually receives, from candidate item selector 120,the information about the particular user for whom the recommendation isgenerated, and the information about the possible candidate items, someof which could be recommended to the user. The collector 132 forwardsthe collected information to processor 134 for further processing.

Further, collector 132 may receive additional requests from processor134 to collect, request and supply any additional information thatprocessor 134 finds necessary while executing a sequence selectionfunction.

In one embodiment, processor 134 receives information about one or moreimmediate candidate items that that can be recommended to the usersimultaneously and immediately, i.e., at the present moment.

The one or more immediate candidate items are the candidate items thatcandidate item selector 120 selected for a particular user from a set ofall potential candidate items available in candidate item database 104.For example, if the particular user is a twenty-year-old male who likessports and the website offers various magazines, then the immediatecandidate items may include those sport magazines that a twenty-year-oldmale who likes sports would find interesting.

In one embodiment, for each immediate candidate item from a set ofimmediate candidate items, processor 134 determines a first-tier of oneor more subsequent possible candidate items for subsequent web pages torecommend to the user in the event that the user selected the particularimmediate candidate item. To determine the first-tier of the subsequentcandidate items for a particular immediate candidate item, processor 134communicates with candidate item selector 120 described above. Thefirst-tier of the subsequent possible candidate items corresponds to thefirst-tier items in a tree of subsequent items generated for theparticular immediate candidate.

After the first-tier of the subsequent candidate items for eachparticular immediate candidate items are selected, processor 134determines, for each particular immediate candidate and each first-tierof the subsequent candidate items for the particular immediatecandidate, a second-tier of one or more subsequent possible candidateitems for subsequent web pages to recommend to the user in the eventthat the user selected the particular immediate candid date item and theparticular first-tier subsequent candidate item for the particularimmediate candidate. To determine the second-tier of the subsequentcandidate items, processor 134 communicates with candidate item selector120 described above. The second-tier of the subsequent possiblecandidate items corresponds to the second-tier items in a tree ofsubsequent items generated for the particular immediate candidate. Theprocess is repeated until all tiers of subsequent candidate items forall particular immediate candidates are generated.

Subsequent Items Tree

A separate sequence of two or more subsequent possible candidate itemsfor the particular immediate candidate item comprises those candidateitems from a set of all candidate items that a user could subsequentlyselect in the event that the user selected the particular immediatecandidate item.

In one embodiment, a separate sequence of subsequent possible candidateitems for a particular immediate candidate item represents a sequence ofevents that represent sequential purchasing of items recommended by thewebsite. For example, if it is known that the users who purchased asubscription to Sports Illustrated also purchased a subscription to ESPNMagazine, and if it is known that the users who purchased a subscriptionto Sports Illustrated, then purchased a subscription to ESPN Magazineand subsequently purchased a subscription to Sports Weekly, then onepossible sequence of subsequent possible candidate items may berepresented as follows:

Sports Illustrated→ESPN Magazine→Sports Weekly

In the same example, if the set of immediate candidate item contains twomagazines, Sports Illustrated and Sporting News, processor 134determines a separate sequence of subsequent possible candidate itemsfor the subsequent web pages to recommend to the user in the event thatthe user selected Sports Illustrated, and determines a separate sequenceof subsequent possible candidate items in the event that the userselected Sporting News. In this example, the separate sequence ofsubsequent possible candidate items for Sport Illustrated may compriseESPN Magazine and Sports Weekly, while the separate sequence ofsubsequent possible candidate items for Sporting News may compriseAthletic Business, Athlon Sports and SportsFan Magazine. Thecorresponding separate sequences of subsequent possible candidate itemsmay be represented as follows:

Sports Illustrated→ESPN Magazine→Sports Weekly Sporting News→AthleticBusiness→Athlon Sports→SportsFan Magazine

The corresponding trees of subsequent possible candidate items may berepresented as follows:

Sequences with Probability Values and Cost Values

In one embodiment, a separate sequence of subsequent possible candidateitems has an associated sequence of probability values. For example, ifit is known that out of 100 users, 50 users purchased SportsIllustrated, 25 users who purchased Sports Illustrated also purchasedESPN Magazine, and 20 users who purchased Sports Illustrated andsubsequently purchased ESPN Magazine also purchased Sports Weekly, thenone possible sequence of subsequent possible candidate items and theassociated sequence of probability values may be as follows:

Sports Illustrated (50%)→ESPN Magazine (25%)→Sports Weekly (20%)

In one embodiment, a separate sequence of subsequent possible candidateitems has an associated sequence of cost values. For example, it isknown that the price of an annual subscription to Sports Illustrated is$39.00, the price of an annual subscription to ESPN Magazine is $26.00,and the price of an annual subscription to Sports Weekly is $39.95, thenone possible sequence of subsequent possible candidate items and theassociated sequence of cost values may be as follows:

Sports Illustrated ($39.00)→ESPN Magazine ($26.00)→Sports Weekly($39.95)

While there are many approaches for representing separate sequencescumulatively, according to one example, probability values and costvalues associated with one separate sequence may be as follows:

Sports Illustrated (50%; $39.00)→ESPN Magazine (25%; $26.00)→SportsWeekly (20%; $39.95)

The processing performed by processor 134 may also involve requestingany additional information necessary for determining the separatesequences, described above, collecting statistical information, sendingupdated information to collector 132, etc.

Further, the processing performed by processor 134 may involverestructuring or reorganizing the separate sequences, described above,and reformatting the information about the separate sequences to aformat that is compatible with the format used by other modules ofrecommendation engine 110.

Processor 134 may communicate with candidate item selector 120, sequenceevaluator 140 and webpage generator 150 each time any of them requiresany information about the candidate items, the users and the separatesequences.

Sequence Evaluator

In one embodiment, sequence evaluator 140 is a machine-implementedmodule that further evaluates sequences of subsequent items determinedby sequence selector 130. Sequence evaluator 140 generates itsrecommendation by taking into consideration not just one or moreimmediate candidate items that can be recommended to the user at aparticular moment, but also the separate sequences of subsequentpossible candidate items generated for each particular immediatecandidate item for subsequent web pages to recommend to the user in theevent that the user selects the particular immediate candidate item.

Sequence evaluator 140 takes into consideration a sequence of possibleevents for each of the particular immediate candidate items and selectsthat particular immediate candidate item for which the associatedsequence of events could generate the most beneficial outcome. Forexample, if the objective of the recommendation is to maximize a valueof revenue generated by a sale of the books, then sequence evaluator 140may select that particular immediate candidate item for which thecombined value of the sale of the particular immediate candidate itemand the associated sequence of possible sequent sales would generate thehighest revenue.

In one embodiment, sequence evaluator 140 comprises collector 142 andprocessor 144. Collector 142 collects, requests and updates informationthat pertains to the selection of candidate items by candidate itemselector 120 and sequence selector 130, while processor 144 processesthe information supplied by collector 142, and provides the informationto webpage generator 150.

In one embodiment, sequence evaluator 140 is invoked by recommendationengine 110 after sequence selector 130 generated separate sequences oftwo or more subsequent possible candidate items for each of theparticular immediate candidate items, as described above.

Using the information generated by sequence selector 130, collector 142collects additional information from user database 102, and candidateitem database 104 that processor 144 requires to further process to therecommendation request. For example, collector 142 may determine, selectand retrieve from candidate item database 104 information about thevalue function that processor 144 may use to determine or compute a“value” of each immediate candidate item, and a “value” of each separatesequence of subsequent possible candidate items for each particularimmediate candidate item.

Values computed using value functions may represent a variety ofmeasures. Some measures may represent a revenue value that can begenerated by sales of the recommended candidate items. For example, thevalue computed using the value function of a particular immediatecandidate item may correspond to the revenue generated by a sale of theparticular candidate item and a sale of the subsequent possiblecandidate items to a user. In a magazine-selling example, collector 142may retrieve information about the prices of annual subscriptions foreach of the immediate candidate item selected by candidate item selector120 and the prices of each subsequent possible candidate item in each ofthe separate sequence for each particular immediate candidate itemselected by sequence selector 130. Alternatively, that information maybe provided to processor 144 by sequence selector 130.

Another measure may represent a maximum number of subsequent selectionsthat the user can make subsequently to select each of the particularimmediate candidate item one at the time. For example, the valuecomputed using the value function of a particular immediate candidateitem may correspond to the total number of candidate item selection thatthe user can make in the event that the user selected the particularimmediate candidate item. In a magazine-selling example, collector 142may retrieve information about the length of each of the separatesequences of the subsequent possible candidate items, determined foreach of the particular magazine, and for recommendation in the eventthat the user selected the particular immediate magazine.

Other examples of specific measures used by the value functions include,but are not limited to, a total quantity of sales, a total quantity ofrecommendation, a value of a probable revenue, a value of a probablenumber of sales, a value of a probable number of recommendation, etc.

Example of Computing a Value Function

In a magazine-selling example, processor 144 computes a value functionfor each of the sequences, described above, i.e.:

Sports Illustrated→ESPN Magazine→Sports Weekly Sporting News→AthleticBusiness→Athlon Sports→SportsFan Magazine

In one embodiment, processor 144 computes a value function by adding thecorresponding costs associated with each candidate item. For example, ifthe prices associated with each of the magazines in one of the abovesequences are as follows:

Sports Illustrated ($39.00) ESPN Magazine ($26.00) Sports Weekly($39.95),

then the value associated with that particular sequence is$39.00+$26.00+$39.95=$104.95. The value of $104.95 corresponds to therevenue that would be generated if, after Sports Illustrated wasrecommended to the user, the user not only purchased a subscription toSports Illustrated but also purchased a subscription to ESPN Magazine,and then, purchased a subscription to Sports Weekly. The process isrepeated for each of the separate sequences for each of the particularimmediate candidate item until all the values associated with each ofthe separate sequences are known.

If the system policy is to maximize the value of revenue generated bythe sales, then processor 144 may recommend that particular immediatecandidate item for which the value function computed for the associatedseparate sequence of subsequent possible candidate items returns thehighest value. For example, if the value for one separate sequence:

(Sports Illustrated→ESPN Magazine→Sports Weekly) is $104.95,

and for another separate sequence:

(Sporting News→Athletic Business→Athlon Sports→SportsFan Magazine) is$55.55,

for which details not provided here, then the separate sequence ofSports Illustrated→ESPN Magazine→Sports Weekly is more desirable sinceit could potentially generate a higher value of revenue that theseparate sequence of Sporting News→Athletic Business→AthlonSports→SportsFan Magazine. Accordingly, in this example, processor 144may recommend that Sports Illustrated be a particular immediatecandidate item for a first web page to recommend to the user.

The above approach may also take into consideration probability valuesassociated with each of the separate sequences for each of the immediatecandidate items. For example, if a separate sequence of subsequentpossible candidate items for “Sports Illustrated” has the followingassociated probability values:

Sports Illustrated (50%; $39.00)→ESPN Magazine (25%; $26.00)→SportsWeekly (20%; $39.95),

then, one of the possible probable values associated with thatparticular sequence is:

0.5*$39.00+0.25*$26.00+0.2*$39.95=$19.5+$6.5+$7.99=$33.99

Using the selected approach, processor 144 computes probable values foreach of the separate sequences and selects that sequence which mets therecommendation criteria the best. Accordingly, in this example,processor 144 may recommend that particular immediate candidate item tothe user that has the highest associated value.

Click-Through-Rate Approach

Implementations of calculating the value function may vary. One of theexemplary implementation may be based on the “click-through-rate” (CTR)approach, which is a way of measuring the success of online productadvertising. A CTR may be computed by dividing the number of users whoselected a particular advertisement on a web page by the number of timesthe particular advertisement was presented to the user in one browsingsession. Other approaches may be implemented as well.

In one embodiment, the system policy allows recommending more than oneparticular immediate candidate item to the user at each given step. Forexample, processor 144 may use threshold values that allow determiningwhich of the immediate candidate items have associated values that meetthe threshold requirements. In a magazine-selling example, processor 144may select for recommendation those immediate candidate items for whichannual-subscription-sales of all candidate items in the correspondingseparate sequences generate the revenue of at least $100.00. Hence, in aparticular example, there might be more than just one particularimmediate candidate item that met the threshold requirement, more thanjust one particular immediate candidate item may be recommended to theuser in one recommendation step.

In one embodiment, processor 144 analyzes a limited quantity ofimmediate candidate items and analyzes a limited quantity of subsequentpossible candidate items in the separate sequences of the separatecandidate items. For example, processor 144 may look at no more than “D”immediate candidate items, where “D” is a variable with an associatedvalue. Furthermore, processor 144 may analyze the separate sequencesthat contain no more than “L” subsequent candidate items, where “L” is avariable with an associated value. Hence, a path corresponding to aparticular subsequent sequence may have length no larger than “L.”Parameter “L” is often referred to as a look-ahead parameter.

For example, if “D” has a value of 2 and “L” has a value of 3, thenprocessor 144 can analyze only two immediate candidate items at thetime, and for each immediate candidate item, processor 144 analyzes theseparate sequences that contain no more than 3 candidate items. This canbe illustrates in the following example, in which each of two immediatecandidate items has an associated tree containing subsequent choicesthat will be analyzed by processor 144:

In the above example, the sequences provided for immediate candidateitem #1 are the following sequences:

A→B→D A→C→E F→G→I F→H

The sequences provided for immediate candidate item #2 are as follows:

K→L→N K→L→O K→L→P K→M→R S→T→U S→T→V Recursion of the Sequence Evaluation

The depth of the recursion is controlled by the look-ahead parameter“L.” In one implementation, the CTR algorithm computes aclick-through-rate for each of the sequence by taking into considerationonly “L” items in each sequence. Processor 144 selects that candidateitem which has a path with the highest associated overallclick-through-rate value. Implementation examples may include anapproach based on the Viterbi algorithm, although other approaches maybe used as well.

In the above example, if processor 144 determines that item “A” shouldbe recommended in the first step and the user indeed selected therecommended item “A,” then the user information is updated, candidateitem information is updated and selecting history is updated as well.Then, the processes of generating the immediate candidate items startsfrom the beginning, and the generating of the separate subsequentcandidate items starts from the beginning as well.

The process of generating the immediate candidate items and the processof generating of separate sequences of the subsequent candidate itemsstart from their respective beginnings even if the user does not selectthe recommended item “A.” In fact, the processes of candidate itemselection, sequence selection and sequence evaluation can be repeatedeach time the new information about the user selection and about theselected candidate items is obtained by the recommendation engine 110.

Information about the recommendations determined by processor 144 issent to web page generator 150 for further processing. In addition, theinformation about the recommendation may be sent to correspondingdatabases, such as user database 102, and candidate item database 104.The recommendation information is used to update the user preferences,the user selection history, the user state, etc.

Webpage Generator

In one embodiment, webpage generator 150 is a machine-implemented modulethat receives one or more candidate item recommendations, and generatesa webpage with the recommendations for the user. Webpage generator 150is invoked each time a set of recommended items is determined bysequence evaluator 140.

Webpage generator 150 comprises a collector 152 and a processor 154.Collector 152 collects, requests and updates information that pertainsto the recommendation generated and supplied by sequence evaluator 140and that is stored in user database 102, and candidate item database104.

Collector 152 collects information about the recommended particularimmediate candidate item(s) for a first web page to recommend to theuser, and forwards the information to processor 154.

Collector 152 may also retrieve software modules, classes of objects,variables and code modules that processor 154 may need to generate theweb page with the recommendations.

Processor 154 receives recommendation information and generates a webpage that contains the recommendations to the user and sends the webpage over the Internet to the user. Processor 154 may execute the webpage generation code, instantiate the objects and execute the steps ofthe web page generation.

Example Selection of Immediate Candidate Items

FIG. 2 depicts an example of technique for determining, for a user,candidate item recommendations, by determining immediate candidate itemsthat are suitable for recommendation presently, and determining one ormore subsequent candidate items that would be suitable forrecommendation in the future in the event that the immediate candidateitem of that sequence actually is presently recommended, in accordancewith an embodiment of the invention.

In block 202, recommendation engine 110 determines a plurality ofimmediate candidate items that are suitable for recommendation to a userpresently. The determination of the immediate candidate items isperformed based on the information about the user profile, preferences,web browsing history, purchasing history, etc., and the informationabout candidate items that the website is able to recommend.

In block 204, recommendation engine 110 determines, for each of theimmediate candidate items, one or more separate sequences of subsequentpossible candidate items for subsequent web pages to recommend to theuser in the event that the user selected the particular immediatecandidate item. A sequence of subsequent possible candidate items may berepresented as a tree of the subsequent items selected for a particularimmediate candidate item and “rooted” at the particular immediatecandidate item.

The determination of the sequences of subsequent candidate items isperformed based on the information about the user profile, preferences,web browsing history, purchasing history, etc., and the informationabout candidate items that the website is able to recommend.

Each of the subsequent possible candidate items enlisted in a particularseparate sequence is a candidate item that is not going to beimmediately recommended to the user by the first web page. Thesubsequent possible candidate items enlisted in the particular separatesequence associated with the particular immediate candidate item arejust possible candidate items that may be recommended to the user in theevent that the user selected the particular immediate candidate itemfirst.

In block 206, recommendation engine 110 selects a particular immediatecandidate item from the plurality of the immediate candidate items forthe first web page to recommend to the user. The determination of theparticular candidate item is performed by computing a specific value ofa value function for each of the particular immediate candidate item andits associated sequence, and, based on the computed value, identifyingthat particular immediate candidate item that is the stronger contenderto be recommended to the user. Examples of specific approaches forcomputing the specific value of the value function are provided above.

In block 208, recommendation engine 110 generates and sends over theInternet to the user a first website containing the particular immediatecandidate item determined to be the best recommendation choice for theparticular user at the particular time.

Alternatively, recommendation engine 110 may determine more than oneparticular immediate candidate item to be recommended to the user. Thus,the first web page may contain two or more particular immediatecandidate items that recommendation engine 110 selected as the bestchoices to be recommended to the user at the particular time. Forexample, if the function value is defined in such a way that comparesthe special values with a threshold value, then more than one immediatecandidate item may have the special values that exceed the thresholdvalue, and thus may become the recommended immediate candidate items.

Example of Generating Book Candidate Items to Maximize Revenue

The recommendation approach described above may be implemented in awebsite server that advertises and sells online books to the users. Theexemplary website may have access to one or more databases containinginformation about the online users who have registered with the websiteand provided their preferences, profiles, interests, browsing andpurchasing history, etc. The website may also have access to one or moredatabases containing information about the books that the website maysell online, as well as statistical information about each offered book,etc.

Upon receiving a request for recommendation, the recommendation enginegathers characteristics of the user for whom the recommendation is beingderived and determines a set of immediate book candidate items that maybe recommended to the user at the particular time. The set of immediatebook candidate items will most likely meet the user's preference to someextend and, to some extend, satisfy the recommendation policiesimplemented in the recommendation engine.

In the next steps, the recommendation engine determines, for eachimmediate book candidate item, a separate sequence of two or moresubsequent possible books for subsequent web pages to recommend to theuser in the event that the user selected the particular immediate bookcandidate item. The recommendation engine uses statistical informationabout the users and the book purchases to determine which subsequentbooks the users select after selecting each of the particular immediatebook, and then after selecting the subsequent books, and so forth.

In determining the ultimate book recommendation to the user at theparticular moment, the recommendation engine takes into considerationnot only each of the immediate book candidate items, but also possiblesubsequent recommendations that can be made by the subsequent web pagesin the future. For example, if there are two immediate book candidateitems, the trees of the subsequent possible candidate items for each ofthe immediate book candidate items may be depicted as follows:

In the next steps, the recommendation engine computes a value for eachof the identified separate sequences using, for example, the CTRapproach, and selects the separate sequences that has the highest value,or for which the values is above a predefined threshold. For example,the recommendation engine may determine that the tree of the sequencesfor the first item (see the tree for the immediate book candidate item#1, above) can potentially generate revenue of $555.00 if the userindeed selects and purchases each and every book depicted in the firsttree, while the tree of the sequences for the second item (see the treefor the immediate book candidate item #2, above) can potentiallygenerate revenue of $600.00 if the user indeed selects and purchaseseach end every book depicted in the second tree. If the recommendationengine tries to maximize the value of revenue that can be potentiallygenerated by the sale of the online books by the website, thenrecommending the second immediate book candidate item is the best choiceat the moment, since it has the highest possible revenue value.Therefore, in this example, the second immediate book candidate itemwill be most likely recommended to the user at the present time.

In some implementations, the recommendation engine may present more thanone immediate book candidate item at the time. In other implementations,the recommendation engine may rather optimize the number of potentiallysold books than the total value of potentially generated revenue.

Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 3 is a block diagram that illustrates a computersystem 300 upon which an embodiment of the invention may be implemented.Computer system 300 includes a bus 302 or other communication mechanismfor communicating information, and a hardware processor 304 coupled withbus 302 for processing information. Hardware processor 304 may be, forexample, a general purpose microprocessor.

Computer system 300 also includes a main memory 306, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 302for storing information and instructions to be executed by processor304. Main memory 306 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 304. Such instructions, when stored innon-transitory storage media accessible to processor 304, rendercomputer system 300 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 300 further includes a read only memory (ROM) 308 orother static storage device coupled to bus 302 for storing staticinformation and instructions for processor 304. A storage device 310,such as a magnetic disk or optical disk, is provided and coupled to bus302 for storing information and instructions.

Computer system 300 may be coupled via bus 302 to a display 312, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 314, including alphanumeric and other keys, is coupledto bus 302 for communicating information and command selections toprocessor 304. Another type of user input device is cursor control 316,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 304 and forcontrolling cursor movement on display 312. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 300 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 300 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 300 in response to processor 304 executing one or more sequencesof one or more instructions contained in main memory 306. Suchinstructions may be read into main memory 306 from another storagemedium, such as storage device 310. Execution of the sequences ofinstructions contained in main memory 306 causes processor 304 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperation in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 310.Volatile media includes dynamic memory, such as main memory 306. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, and EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 302. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 304 for execution. For example,the instructions may initially be carried on a magnetic disk or solidstate drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 300 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 302. Bus 302 carries the data tomain memory 306, from which processor 304 retrieves and executes theinstructions. The instructions received by main memory 306 mayoptionally be stored on storage device 310 either before or afterexecution by processor 304.

Computer system 300 also includes a communication interface 318 coupledto bus 302. Communication interface 318 provides a two-way datacommunication coupling to a network link 320 that is connected to alocal network 322. For example, communication interface 318 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 318 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 318sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 320 typically provides data communication through one ormore networks to other data devices. For example, network link 320 mayprovide a connection through local network 322 to a host computer 324 orto data equipment operated by an Internet Service Provider (ISP) 326.ISP 326 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 328. Local network 322 and Internet 328 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 320and through communication interface 318, which carry the digital data toand from computer system 300, are example forms of transmission media.

Computer system 300 can send messages and receive data, includingprogram code, through the network(s), network link 320 and communicationinterface 318. In the Internet example, a server 330 might transmit arequested code for an application program through Internet 328, ISP 326,local network 322 and communication interface 318.

The received code may be executed by processor 304 as it is received,and/or stored in storage device 310, or other non-volatile storage forlater execution.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

1. A computer-implemented method comprising steps of: determining aplurality of immediate candidate items for a first web page to berecommended to a user; for each immediate candidate item of theplurality of immediate candidate items: (a) determining a separatesequence of two or more subsequent possible candidate items forsubsequent web pages to be recommended to the user in the event that theuser selects the immediate candidate item; (b) associating the separatesequence of two or more subsequent possible candidate items with theimmediate candidate item; (c) determining a potential revenue valuegenerated in the event that the user selects the immediate candidateitem and each of the subsequent possible candidate items from theseparate sequence that is associated with the immediate candidate item;selecting for the first web page, from the plurality of immediatecandidate items, a particular immediate candidate item for which thepotential revenue value is the highest; recommending to the user theparticular immediate candidate item in the first web page; wherein themethod is performed by one or more special-purpose computing devices. 2.The method of claim 1, wherein determining a plurality of immediatecandidate items for recommendation further comprises determining ahistory log associated with the user that contains information aboutselections that the user made in the past.
 3. The method of claim 1,wherein determining a plurality of immediate candidate items forrecommendation is performed each time the user makes a selection or apurchase of a candidate item.
 4. The method of claim 2, wherein thehistory log is updated each time the user selects an immediate candidateitems from the plurality of candidate items.
 5. The method of claim 2,wherein the history log is updated each time the user selects animmediate candidate item from the plurality of candidate items andtransacts to purchase the immediate candidate item.
 6. The method ofclaim 1, further comprising selecting for the first web page, from theplurality of immediate candidate items, the particular immediatecandidate item for which a maximum number of subsequent selectionsexceeds a threshold.
 7. The method of claim 1, wherein the potentialrevenue value is a function of an individual cost of the immediatecandidate item and each of the subsequent candidate items that might bepresented to the user if the user selects the immediate candidate item,and an individual probability that each of the subsequent candidateitems will be selected by the user; wherein the individual cost of acandidate item represents a revenue that is generated if the candidateitem is selected and purchased by the user; wherein the individualprobability of a candidate item represents a click-through-rate that theuser might select and purchase the candidate item.
 8. An apparatuscomprising: one or more processors; a candidate item selector unitconfigured to perform: determining a plurality of immediate candidateitems for a first web page to recommend to a user; a sequence selectorunit configured to perform: for each immediate candidate item of theplurality of immediate candidate items: (a) determining a separatesequence of two or more subsequent possible candidate items forsubsequent web pages to be recommended to the user in the event that theuser selects the immediate candidate item; (b) associating the separatesequence of two or more subsequent possible candidate items with theimmediate candidate item; (c) determining a potential revenue valuegenerated in the event that the user selects the immediate candidateitem and each of the subsequent possible candidate items from theseparate sequence that is associated with the immediate candidate item;a sequence evaluator unit configured to perform: selecting for the firstweb page, from the plurality of immediate candidate items, a particularimmediate candidate item for which the potential revenue value is thehighest; a webpage generator unit configured to perform: recommending tothe user the particular immediate candidate item in the first web page.9. The apparatus of claim 8, wherein determining a plurality ofimmediate candidate items for recommendation further comprisesdetermining a history log associated with the user that containsinformation about selections that the user made in the past.
 10. Theapparatus of claim 8, wherein determining a plurality of immediatecandidate items for recommendation is performed each time the user makesa selection or a purchase of a candidate item.
 11. The apparatus ofclaim 9, wherein the history log is updated each time the user selectsan immediate candidate items from the plurality of candidate items. 12.The apparatus of claim 9, wherein the history log is updated each timethe user selects an immediate candidate item from the plurality ofcandidate items and transacts to purchase the immediate candidate item.13. The apparatus of claim 8, wherein the sequence evaluator unit isfurther configured to select for the first web page, from the pluralityof immediate candidate items, the particular immediate candidate itemfor which a maximum number of subsequent selections exceeds a threshold.14. The apparatus of claim 8, wherein the potential revenue value is afunction of an individual cost of the immediate candidate item and eachof the subsequent candidate items that might be presented to the user ifthe user selects the immediate candidate item, and an individualprobability that each of the subsequent candidate items will be selectedby the user; wherein the individual cost of a candidate item representsa revenue that is generated if the candidate item is selected andpurchased by the user; wherein the individual probability of a candidateitem represents a click-through-rate that the user might select andpurchase the candidate item.
 15. A non-transitory computer-readablestorage medium that stores instructions which, when executed by one ormore processors, cause the one or more processors to perform stepscomprising: determining a plurality of immediate candidate items for afirst web page to be recommended to a user; for each immediate candidateitem of the plurality of immediate candidate items: (a) determining aseparate sequence of two or more subsequent possible candidate items forsubsequent web pages to be recommended to the user in the event that theuser selects the immediate candidate item; (b) associating the separatesequence of two or more subsequent possible candidate items with theimmediate candidate item; (c) determining a potential revenue valuegenerated in the event that the user selects the immediate candidateitem and each of the subsequent possible candidate items from theseparate sequence that is associated with the immediate candidate item;selecting for the first web page, from the plurality of immediatecandidate items, a particular immediate candidate item for which thepotential revenue value is the highest; recommending to the user theparticular immediate candidate item in the first web page.
 16. Thecomputer-readable medium of claim 15, wherein the instructions thatcause the one or more processors to perform determining a plurality ofimmediate candidate items for recommendation further compriseinstructions, which cause the one or more processors to performdetermining a history log associated with the user that containsinformation about selections that the user made in the past.
 17. Thecomputer-readable medium of claim 15, wherein the instructions thatcause the one or more processors to perform determining a plurality ofimmediate candidate items for recommendation are executed each time theuser makes a selection or a purchase of a candidate item.
 18. Thecomputer-readable medium of claim 16, wherein the history log is updatedeach time the user selects an immediate candidate items from theplurality of candidate items.
 19. The computer-readable medium of claim16, wherein the history log is updated each time the user selects animmediate candidate item from the plurality of candidate items andtransacts to purchase the immediate candidate item.
 20. Thecomputer-readable medium of claim 15, further comprising instructionswhich, when executed, cause the one or more processors to performselecting for the first web page, from the plurality of immediatecandidate items, the particular immediate candidate item for which amaximum number of subsequent selections exceeds a threshold.
 21. Thecomputer-readable medium of claim 15, wherein the potential revenuevalue is a function of an individual cost of the immediate candidateitem and each of the subsequent candidate items that might be presentedto the user if the user selects the immediate candidate item, and anindividual probability that each of the subsequent candidate items willbe selected by the user; wherein the individual cost of a candidate itemrepresents a revenue that is generated if the candidate item is selectedand purchased by the user; wherein the individual probability of acandidate item represents a click-through-rate that the user mightselect and purchase the candidate item.
 22. The computer-implementedmethod of claim 1, wherein the steps (a)-(c) are performed for eachimmediate candidate item of the plurality of immediate candidate itemsbefore recommending any item from the plurality of immediate candidateitems to the user.
 23. The apparatus of claim 8, wherein the sequenceselector unit is further configured to perform the steps (a)-(c) foreach immediate candidate item of the plurality of immediate candidateitems before recommending any item from the plurality of immediatecandidate items to the user.
 24. The non-transitory computer-readablestorage medium of claim 15, wherein the instructions for performing thesteps (a)-(c) further comprise instructions which, when executed, causeperforming the steps (a)-(c) for each immediate candidate item of theplurality of immediate candidate items before recommending any item fromthe plurality of immediate candidate items to the user.